Encoding natural scenes with neural circuits with random thresholds
Encoding natural scenes with neural circuits with random thresholds
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DOI：
10.1016/j.visres.2010.03.015
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发表时间：
2010-10-01
期刊：
影响因子：
1.8
通讯作者：
Zhou，Yiyin
中科院分区：
文献类型：
--
作者：
Lazar，Aurel A.;Pnevmatikakis，Eftychios A.;Zhou，Yiyin
我们提出了一种重建自然视频场景的通用框架，该视频场景由具有随机阈值的尖峰神经电路群体编码。自然场景被建模为属于三角多项式空间的时空函数。视觉编码系统包括一组过滤器，模拟视觉感受野，与一组神经回路级联，模拟早期视觉系统中的编码。所考虑的神经元模型包括整合-放电神经元和具有阈值-放电尖峰机制的开-关神经元对。假设所有阈值都是随机的。我们证明，对于时变和时空变化的刺激，神经放电类似于对刺激进行噪声测量。我们将重构问题描述为有限维向量空间中适当的代价泛函的最小化问题，并给出了一个显式的刺激恢复算法。我们还利用再生核Hilbert空间中的光滑样条理论给出了一般解。我们提供了合成视频和自然场景的例子，并证明了重建的质量随着神经元阈值变异性的增加而优雅地下降。
We present a general framework for the reconstruction of natural video scenes encoded with a population of spiking neural circuits with random thresholds. The natural scenes are modeled as space-time functions that belong to a space of trigonometric polynomials. The visual encoding system consists of a bank of filters, modeling the visual receptive fields, in cascade with a population of neural circuits, modeling encoding in the early visual system. The neuron models considered include integrate-and-fire neurons and ON–OFF neuron pairs with threshold-and-fire spiking mechanisms. All thresholds are assumed to be random. We demonstrate that neural spiking is akin to taking noisy measurements on the stimulus both for time-varying and space-time-varying stimuli. We formulate the reconstruction problem as the minimization of a suitable cost functional in a finite-dimensional vector space and provide an explicit algorithm for stimulus recovery. We also present a general solution using the theory of smoothing splines in Reproducing Kernel Hilbert Spaces. We provide examples of both synthetic video as well as for natural scenes and demonstrate that the quality of the reconstruction degrades gracefully as the threshold variability of the neurons increases.